The influence of winter oilseed rape (Brassica napus ssp. oleifera var. biennis) cultivar and grass genotype on the competitive balance between crop and grass weeds

Four experiments conducted over three seasons (2002–05) at the Crops Research Unit, University of Reading, investigated effects of canopy management of autumn sown oilseed rape (Brassica napus L. ssp. oleifera var. biennis (DC.) Metzg.) on competition with grass weeds. Emphasis was placed on the effect of the crop on the weeds. Rape canopy size was manipulated using sowing date, seed rate and the application of autumn fertilizer. Lolium multiflorum Lam., L.rboucheanum Kunth and Alopecurus myosuroides Huds. were sown as indicative grass weeds. The effects of sowing date, seed rate and autumn nitrogen on crop competitive ability were correlated with rape biomass and fractional interception of photosynthetically active radiation (PAR) by the rape floral layer, to the extent that by spring there was good evidence of crop: weed replacement. An increase in seed rate up to the highest plant densities tested increased both rape biomass and competitiveness, e.g. in 2002/3, L. multiflorum head density was reduced from 539 to 245 heads/m2 and spikelet density from 13 170 to 5960 spikelets/m2 when rape plant density was increased from 16 to 81 plants/m2. Spikelets/head of Lolium spp. was little affected by rape seed rate, but the length of heads of A. myosuroides was reduced by 9%when plant density was increased from 29–51 plants/m2. Autumn nitrogen increased rape biomass and reduced L. multiflorum head density (415 and 336 heads/m2 without and with autumn nitrogen, respectively) and spikelet density (9990 and 8220 spikelets/m2 without and with autumn nitrogen, respectively). The number of spikelets/head was not significantly affected by autumn nitrogen. Early sowing could increase biomass and competitiveness, but poor crop establishment sometimes overrode the effect. Where crop and weed establishment was similar for both sowing dates, a 2-week delay (i.e. early September to mid-September) increased L. multiflorum head density from 226 to 633 heads/m2 and spikelet density from 5780 to 15 060 spikelets/m2.

The influence of winter oilseed rape (Brassica napus ssp oleifera var. biennis) cultivar and grass genotype on the competitive balance between crop and grass weeds

Three experiments conducted over two years (2002-04) at the Crops Research Unit, University of Reading, investigated competition between autumn sown oilseed rape cultivars (Brassica napus L. ssp. oleifera var. biennis (DC.) Metzg.) and Lolium multiflorum Lam., L. x boucheanum Kunth and Alopecurus myosuroides Huds., sown as indicative grass weeds. Rape cultivar (cv.) had a substantial effect on grass weed seed return. Over the six cultivars tested, L. multiflorum spikelet production ranged from just under 400 spikelets/m(2) in the presence of cv. Winner to nearly 5800 in competition with cv. Lutin. Cultivar competitiveness was associated with high biomass, large dense floral layers and early stem extension. There was some evidence of differential competitive tolerance between rape cultivars. The results suggested that rape cultivars could be screened for competitiveness by measuring floral layer interception of photosynthetic active radiation. L. x boucheanum cultivars varied in ability to compete with rape. In the absence of inter-specific competition, spikelet density was similar for Aberecho and Polly (circa 31000 spikelets/m(2)) but when grown with rape Polly outyielded Aberecho (i.e. 12 090 and 7990 spikelets/m(2) respectively).

Exploring block construction and mental imagery: evidence of atypical orientation discrimination in Williams syndrome

The visuospatial perceptual abilities of individuals with Williams syndrome (WS) were investigated in two experiments. Experiment I measured the ability of participants to discriminate between oblique and between nonoblique orientations. Individuals with WS showed a smaller effect of obliqueness in response time, when compared to controls matched for nonverbal mental age. Experiment 2 investigated the possibility that this deviant pattern of orientation discrimination accounts for the poor ability to perform mental rotation in WS (Farran, Jarrold, & Gathercole, 2001). A size transformation task was employed, which shares the image transformation requirements of mental rotation, but not the orientation discrimination demands. Individuals with WS performed at the same level as controls. The results suggest a deviance at the perceptual level in WS, in processing orientation, which fractionates from the ability to mentally transform images.

The role of perirhinal cortex in visual discrimination learning for visual secondary reinforcement in rats

Perirhinal cortex in monkeys has been thought to be involved in visual associative learning. The authors examined rats' ability to make associations between visual stimuli in a visual secondary reinforcement task. Rats learned 2-choice visual discriminations for secondary visual reinforcement. They showed significant learning of discriminations before any primary reinforcement. Following bilateral perirhinal cortex lesions, rats continued to learn visual discriminations for visual secondary reinforcement at the same rate as before surgery. Thus, this study does not support a critical role of perirhinal cortex in learning for visual secondary reinforcement. Contrasting this result with other positive results, the authors suggest that the role of perirhinal cortex is in "within-object" associations and that it plays a much lesser role in stimulus-stimulus associations between objects.

Improvement of classification accuracy for stochastic discrimination - Multi-class classification

In this paper, an improved stochastic discrimination (SD) is introduced to reduce the error rate of the standard SD in the context of multi-class classification problem. The learning procedure of the improved SD consists of two stages. In the first stage, a standard SD, but with shorter learning period is carried out to identify an important space where all the misclassified samples are located. In the second stage, the standard SD is modified by (i) restricting sampling in the important space; and (ii) introducing a new discriminant function for samples in the important space. It is shown by mathematical derivation that the new discriminant function has the same mean, but smaller variance than that of standard SD for samples in the important space. It is also analyzed that the smaller the variance of the discriminant function, the lower the error rate of the classifier. Consequently, the proposed improved SD improves standard SD by its capability of achieving higher classification accuracy. Illustrative examples axe provided to demonstrate the effectiveness of the proposed improved SD.

Optimal discrimination and classification of THz spectra in the wavelet domain

In rapid scan Fourier transform spectrometry, we show that the noise in the wavelet coefficients resulting from the filter bank decomposition of the complex insertion loss function is linearly related to the noise power in the sample interferogram by a noise amplification factor. By maximizing an objective function composed of the power of the wavelet coefficients divided by the noise amplification factor, optimal feature extraction in the wavelet domain is performed. The performance of a classifier based on the output of a filter bank is shown to be considerably better than that of an Euclidean distance classifier in the original spectral domain. An optimization procedure results in a further improvement of the wavelet classifier. The procedure is suitable for enhancing the contrast or classifying spectra acquired by either continuous wave or THz transient spectrometers as well as for increasing the dynamic range of THz imaging systems. (C) 2003 Optical Society of America.

On improvement of classification accuracy for stochastic discrimination

Stochastic discrimination (SD) depends on a discriminant function for classification. In this paper, an improved SD is introduced to reduce the error rate of the standard SD in the context of a two-class classification problem. The learning procedure of the improved SD consists of two stages. Initially a standard SD, but with shorter learning period is carried out to identify an important space where all the misclassified samples are located. Then the standard SD is modified by 1) restricting sampling in the important space, and 2) introducing a new discriminant function for samples in the important space. It is shown by mathematical derivation that the new discriminant function has the same mean, but with a smaller variance than that of the standard SD for samples in the important space. It is also analyzed that the smaller the variance of the discriminant function, the lower the error rate of the classifier. Consequently, the proposed improved SD improves standard SD by its capability of achieving higher classification accuracy. Illustrative examples are provided to demonstrate the effectiveness of the proposed improved SD.